Molecular descriptor driven <scp>QSPR</scp> modeling of Papp, <scp>TEER</scp> and Efflux Ratio from Caco‐2 cells using machine learning for various phytochemicals

BACKGROUND: The present study aimed to develop and validate quantitative structure-property relationship (QSPR) models for predicting permeability related bioavailability indicators including apparent permeability (Papp), trans-epithelial electrical resistance (TEER) and efflux ratio (ER) based on molecular descriptors (n = 5003) of 83 phytochemicals. A rigorous workflow integrating feature selection and 10 regression algorithms were employed to assess predictive performance. RESULTS: Pearson correlation coefficient analysis revealed significant relationships among the three indictors. Among the models, a stacking ensemble, including CatBoost, LightGBM and Gradient Boosting as base learners and linear regression as the meta-model, achieved strong predictive accuracy for Papp and ER, at the same time as showing moderate but highly variable performance for TEER. SHapley Additive exPlanations (i.e. SHAP)-based feature importance analysis provided insight into key molecular descriptors associated with electronic, topological and branching. CONCLUSION: These results demonstrate the utility of interpretability tools in constructing robust and explainable QSPR models for bioavailability prediction in various phytochemicals. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.